{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arcpy,pandas\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy\n",
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = arcpy.da.FeatureClassToNumPyArray(\"./data/weather.shp\",\n",
    "                                       [\"mean\",\"SHAPE@X\",\"SHAPE@Y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import  linear_model\n",
    "from sklearn.preprocessing import  PolynomialFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def Coff(x,y):\n",
    "    minX =min(x)\n",
    "    maxX =max(x)\n",
    "    X=numpy.arange(minX,maxX).reshape([-1,1])\n",
    "    poly_reg =PolynomialFeatures(degree=2)\n",
    "    dx = x.reshape([len(x),1])\n",
    "    X_ploy =poly_reg.fit_transform(dx)\n",
    "    lin_reg_2=linear_model.LinearRegression()\n",
    "    lin_reg_2.fit(X_ploy,y)\n",
    "    plt.scatter(dx,y,color='blue')\n",
    "    plt.plot(X,lin_reg_2.predict(poly_reg.fit_transform(X)),color='red')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Coff(dt[\"SHAPE@Y\"],dt[\"mean\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Coff(dt[\"SHAPE@X\"],dt[\"mean\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
